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Bioengineering
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Robotics in Medical Engineering
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Robotics in Medical Engineering

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biomedical Engineering and Biomaterials".

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 8592

Special Issue Editors

Dr. Jiejunyi Liang

E-Mail Website
Guest Editor
School of Mechanical Science & Engineering, Huazhong University of Science and Technology, Wuhan, China
Interests: wearable robotics
Prof. Dr. Ting Zhang

E-Mail Website
Guest Editor
Robotics and Microsystems Center, Soochow University, Suzhou 215000, China
Interests: multi-fingered hand for robots and prostheses; wearable robot; and human–robot interactions
Special Issues, Collections and Topics in MDPI journals
Dr. Jianjun Meng

E-Mail Website
Guest Editor
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: brain-computer interface; neurorobotics; EEG signal processing; robotic arm control; neural rehabilitation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid development of robot technology has led to its wide application in the medical field, and, due to its desirable features of accuracy, versatility, repeatability and intelligence, medical care and fundamental investigations in the fields of medicine and life science have made significant progress in recent decades. Exoskeletons enable the fast rehabilitation of patients with impaired motor function, as the prosthetic technology gives amputees the possibility to return to normal life. Robotic surgical systems enable people in remote areas to benefit from high-quality medical services through teleoperation technology, and intelligent elderly care robots alleviate the problem of population aging to a certain extent. Additionally, high-performance sensors make it possible for us to deepen the understanding of life, as high-resolution medical imaging equipment and artificial intelligence diagnosis technology can assist doctors in proposing more effective treatment plans. However, in order to further enhance the role of robotics in the medical field, there are still many key issues that need to be solved: human–machine interaction, human motion intention recognition, human motion replication, artificial organs, imaging technology, artificial intelligence, etc. Since the application of robotics in the medical field has received extensive attention from specialists around the world, we are launching this Special Issue, entitled “Robotics in Medical Engineering”, which welcomes original research articles, reviews and short communications including, but not limited to, the abovementioned topics.

Dr. Jiejunyi Liang
Prof. Dr. Ting Zhang
Dr. Jianjun Meng
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • exoskeleton
  • prosthesis
  • robotic surgical systems
  • care robot
  • sensors
  • medical imaging equipment
  • diagnosis technology
  • human–machine interaction
  • human intention recognition
  • human motion replication
  • artificial organs

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Published Papers (4 papers)

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Research

17 pages, 5562 KiB  
Article
A Real-Time Control Method for Upper Limb Exoskeleton Based on Active Torque Prediction Model
by Sujiao Li, Lei Zhang, Qiaoling Meng and Hongliu Yu
Bioengineering 2023, 10(12), 1441; https://doi.org/10.3390/bioengineering10121441 - 18 Dec 2023
Cited by 2 | Viewed by 1872
Abstract
Exoskeleton rehabilitation robots have been widely used in the rehabilitation treatment of stroke patients. Clinical studies confirmed that rehabilitation training with active movement intentions could improve the effectiveness of rehabilitation treatment significantly. This research proposes a real-time control method for an upper limb [...] Read more.
Exoskeleton rehabilitation robots have been widely used in the rehabilitation treatment of stroke patients. Clinical studies confirmed that rehabilitation training with active movement intentions could improve the effectiveness of rehabilitation treatment significantly. This research proposes a real-time control method for an upper limb exoskeleton based on the active torque prediction model. To fulfill the goal of individualized and precise rehabilitation, this method has an adjustable parameter assist ratio that can change the strength of the assist torque under the same conditions. In this study, upper limb muscles’ EMG signals and elbow angle were chosen as the sources of control signals. The active torque prediction model was then trained using a BP neural network after appropriately extracting features. The model exhibited good accuracy on PC and embedded systems, according to the experimental results. In the embedded system, the RMSE of this model was 0.1956 N·m and 94.98%. In addition, the proposed real-time control system also had an extremely low delay of only 40 ms, which would significantly increase the adaptability of human–computer interactions. Full article
(This article belongs to the Special Issue Robotics in Medical Engineering)
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17 pages, 18115 KiB  
Article
High Precision Cervical Precancerous Lesion Classification Method Based on ConvNeXt
by Jing Tang, Ting Zhang, Zeyu Gong and Xianjun Huang
Bioengineering 2023, 10(12), 1424; https://doi.org/10.3390/bioengineering10121424 - 15 Dec 2023
Cited by 4 | Viewed by 1928
Abstract
Traditional cervical cancer diagnosis mainly relies on human papillomavirus (HPV) concentration testing. Considering that HPV concentrations vary from individual to individual and fluctuate over time, this method requires multiple tests, leading to high costs. Recently, some scholars have focused on the method of [...] Read more.
Traditional cervical cancer diagnosis mainly relies on human papillomavirus (HPV) concentration testing. Considering that HPV concentrations vary from individual to individual and fluctuate over time, this method requires multiple tests, leading to high costs. Recently, some scholars have focused on the method of cervical cytology for diagnosis. However, cervical cancer cells have complex textural characteristics and small differences between different cell subtypes, which brings great challenges for high-precision screening of cervical cancer. In this paper, we propose a high-precision cervical cancer precancerous lesion screening classification method based on ConvNeXt, utilizing self-supervised data augmentation and ensemble learning strategies to achieve cervical cancer cell feature extraction and inter-class discrimination, respectively. We used the Deep Cervical Cytological Levels (DCCL) dataset, which includes 1167 cervical cytology specimens from participants aged 32 to 67, for algorithm training and validation. We tested our method on the DCCL dataset, and the final classification accuracy was 8.85% higher than that of previous advanced models, which means that our method has significant advantages compared to other advanced methods. Full article
(This article belongs to the Special Issue Robotics in Medical Engineering)
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25 pages, 6605 KiB  
Article
Synthesis of sEMG Signals for Hand Gestures Using a 1DDCGAN
by Mohamed Amin Gouda, Wang Hong, Daqi Jiang, Naishi Feng, Bin Zhou and Ziyang Li
Bioengineering 2023, 10(12), 1353; https://doi.org/10.3390/bioengineering10121353 - 25 Nov 2023
Viewed by 1612
Abstract
The emergence of modern prosthetics controlled by bio-signals has been facilitated by AI and microchip technology innovations. AI algorithms are trained using sEMG produced by muscles during contractions. The data acquisition procedure may result in discomfort and fatigue, particularly for amputees. Furthermore, prosthetic [...] Read more.
The emergence of modern prosthetics controlled by bio-signals has been facilitated by AI and microchip technology innovations. AI algorithms are trained using sEMG produced by muscles during contractions. The data acquisition procedure may result in discomfort and fatigue, particularly for amputees. Furthermore, prosthetic companies restrict sEMG signal exchange, limiting data-driven research and reproducibility. GANs present a viable solution to the aforementioned concerns. GANs can generate high-quality sEMG, which can be utilised for data augmentation, decrease the training time required by prosthetic users, enhance classification accuracy and ensure research reproducibility. This research proposes the utilisation of a one-dimensional deep convolutional GAN (1DDCGAN) to generate the sEMG of hand gestures. This approach involves the incorporation of dynamic time wrapping, fast Fourier transform and wavelets as discriminator inputs. Two datasets were utilised to validate the methodology, where five windows and increments were utilised to extract features to evaluate the synthesised sEMG quality. In addition to the traditional classification and augmentation metrics, two novel metrics—the Mantel test and the classifier two-sample test—were used for evaluation. The 1DDCGAN preserved the inter-feature correlations and generated high-quality signals, which resembled the original data. Additionally, the classification accuracy improved by an average of 1.21–5%. Full article
(This article belongs to the Special Issue Robotics in Medical Engineering)
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19 pages, 2174 KiB  
Article
Teleoperated Surgical Robot with Adaptive Interactive Control Architecture for Tissue Identification
by Yubo Sheng, Haoyuan Cheng, Yiwei Wang, Huan Zhao and Han Ding
Bioengineering 2023, 10(10), 1157; https://doi.org/10.3390/bioengineering10101157 - 2 Oct 2023
Cited by 3 | Viewed by 2267
Abstract
The remote perception of teleoperated surgical robotics has been a critical issue for surgeons in fulfilling their remote manipulation tasks. In this article, an adaptive teleoperation control framework is proposed. It provides a physical human–robot interaction interface to enhance the ability of the [...] Read more.
The remote perception of teleoperated surgical robotics has been a critical issue for surgeons in fulfilling their remote manipulation tasks. In this article, an adaptive teleoperation control framework is proposed. It provides a physical human–robot interaction interface to enhance the ability of the operator to intuitively perceive the material properties of remote objects. The recursive least square (RLS) is adopted to estimate the required human hand stiffness that the operator can achieve to compensate for the contact force. Based on the estimated stiffness, a force feedback controller is designed to avoid the induced motion and to convey the haptic information of the slave side. The passivity of the proposed teleoperation system is ensured by the virtual energy tank. A stable contact test validated that the proposed method achieved stable contact between the slave robot and the hard environment while ensuring the transparency of the force feedback. A series of human subject experiments was conducted to empirically verify that the proposed teleoperation framework can provide a more smooth, dexterous, and intuitive user experience with a more accurate perception of the mechanical property of the interacted material on the slave side, compared to the baseline method. After the experiment, the design idea about the force feedback controller of the bilateral teleoperation is discussed. Full article
(This article belongs to the Special Issue Robotics in Medical Engineering)
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